Development of a Water Cloud Radiance Model for Use in Training an Artificial Neural Network to Recover Cloud Properties from Sun Photometer Observations

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dc.contributor.authorMeehan, Patrick Jamesen
dc.contributor.committeechairMahan, James R.en
dc.contributor.committeememberNguyen, Vinhen
dc.contributor.committeememberVick, Brian L.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2021-06-10T08:00:56Zen
dc.date.available2021-06-10T08:00:56Zen
dc.date.issued2021-06-09en
dc.description.abstractAs the planetary climate continues to evolve, it is important to build an accurate long-term climate record. State-of-the-art atmospheric science requires a variety of approaches to the measurement of the atmospheric structure and composition. This thesis supports the possibility of inferring cloud properties from sun photometer observations of the cloud solar aureole using an artificial neural network (ANN). Training of an ANN requires a large number of input and output parameter sets. A cloud radiance model is derived that takes into consideration the cloud depth, the mean size of the cloud water particles, and the cloud liquid water content. The cloud radiance model derived here is capable of considering the wavelength of the incident sunlight and the cloud lateral dimensions as parameters; however, here we consider only one wavelength—550 nm—and one lateral dimension—500 m—to demonstrate its performance. The cloud radiance model is then used to generate solar aureole profiles corresponding to the cloud parameters as they would be observed using a sun photometer. Coefficients representative of the solar aureole profiles may then be used as inputs to a trained ANN to infer the parameters used to generate the profile. This process is demonstrated through examples. A manuscript submitted for possible publication based on an early version of the cloud radiance model was deemed naïve by reviewers, ultimately leading to improvements documented here.en
dc.description.abstractgeneralThe Earth's climate is driven by heat from the sun and the exchange of heat between the Earth and space. The role of clouds is paramount in this process. One aspect of "cloud forcing" is cloud structure and composition. Required measures may be obtained by satellite or surface-based observations. Described here is the creation of a numerical model that calculates the disposition of individual bundles of light within water clouds. The clouds created in the model are all described by the mean size of the cloud water droplets, the amount of water in the cloud, and cloud depth. Changing these factors relative to each other changes the amount of light that traverses the cloud and the angle at which the individual bundles of light leave the cloud as measured using a device called a sun photometer. The measured amount and angle of bundles of light leaving the cloud are used to recover the parameters that characterize the cloud; i.e., the size of the cloud water droplets, the amount of water in the cloud, and the cloud depth. Two versions of the cloud radiance model are described.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:31691en
dc.identifier.urihttp://hdl.handle.net/10919/103742en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectMie Scatteringen
dc.subjectMonte Carlo Ray-Traceen
dc.subjectArtificial Neural Networken
dc.titleDevelopment of a Water Cloud Radiance Model for Use in Training an Artificial Neural Network to Recover Cloud Properties from Sun Photometer Observationsen
dc.typeThesisen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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